
import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import ViTMAEForPreTraining, ViTMAEModel
							from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							Union[str, Any]=13    ,						snake_case_			:							Dict=30    ,						snake_case_			:							Tuple=2    ,						snake_case_			:							Optional[Any]=3    ,						snake_case_			:							Optional[Any]=True    ,						snake_case_			:							Dict=True    ,						snake_case_			:							Union[str, Any]=32    ,						snake_case_			:							int=5    ,						snake_case_			:							Optional[int]=4    ,						snake_case_			:							int=37    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[int]=0.1    ,						snake_case_			:							Dict=0.1    ,						snake_case_			:							Optional[int]=10    ,						snake_case_			:							List[Any]=0.02    ,						snake_case_			:							List[Any]=3    ,						snake_case_			:							Dict=0.6    ,						snake_case_			:							Any=None    ,						):
							UpperCamelCase_:				Dict						=    parent
							UpperCamelCase_:				int						=    batch_size
							UpperCamelCase_:				Dict						=    image_size
							UpperCamelCase_:				List[Any]						=    patch_size
							UpperCamelCase_:				Optional[Any]						=    num_channels
							UpperCamelCase_:				Optional[Any]						=    is_training
							UpperCamelCase_:				int						=    use_labels
							UpperCamelCase_:				str						=    hidden_size
							UpperCamelCase_:				Any						=    num_hidden_layers
							UpperCamelCase_:				List[str]						=    num_attention_heads
							UpperCamelCase_:				str						=    intermediate_size
							UpperCamelCase_:				List[str]						=    hidden_act
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Optional[Any]						=    attention_probs_dropout_prob
							UpperCamelCase_:				Any						=    type_sequence_label_size
							UpperCamelCase_:				str						=    initializer_range
							UpperCamelCase_:				Tuple						=    mask_ratio
							UpperCamelCase_:				Union[str, Any]						=    scope

							# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
							# (we add 1 for the [CLS] token)
							UpperCamelCase_:				int						=    (image_size // patch_size) ** 2
							UpperCamelCase_:				Any						=    int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				Optional[int]						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

							UpperCamelCase_:				Dict						=    None
							if self.use_labels:
											UpperCamelCase_:				str						=    ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )

							UpperCamelCase_:				List[Any]						=    self.get_config()

							return config, pixel_values, labels

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							return ViTMAEConfig(
							    image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						intermediate_size=self.intermediate_size    ,						hidden_act=self.hidden_act    ,						hidden_dropout_prob=self.hidden_dropout_prob    ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,						is_decoder=snake_case_    ,						initializer_range=self.initializer_range    ,						mask_ratio=self.mask_ratio    ,						)

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							Tuple   ):
							UpperCamelCase_:				List[str]						=    ViTMAEModel(config=snake_case_   )
							model.to(snake_case_   )
							model.eval()
							UpperCamelCase_:				Optional[int]						=    model(snake_case_   )
							self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)   )

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							str    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				List[str]						=    ViTMAEForPreTraining(snake_case_   )
							model.to(snake_case_   )
							model.eval()
							UpperCamelCase_:				int						=    model(snake_case_   )
							UpperCamelCase_:				str						=    (self.image_size // self.patch_size) ** 2
							UpperCamelCase_:				str						=    self.patch_size**2 * self.num_channels
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)   )

							# test greyscale images
							UpperCamelCase_:				Optional[Any]						=    1
							UpperCamelCase_:				Any						=    ViTMAEForPreTraining(snake_case_   )
							model.to(snake_case_   )
							model.eval()
							UpperCamelCase_:				Optional[int]						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
							UpperCamelCase_:				Union[str, Any]						=    model(snake_case_   )
							UpperCamelCase_:				Optional[Any]						=    self.patch_size**2
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, num_patches, expected_num_channels)   )




			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				List[str]						=    self.prepare_config_and_inputs()
							UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[Any]						=    config_and_inputs
							UpperCamelCase_:				Union[str, Any]						=    {"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[Any]          =			(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			__UpperCamelCase   :    Union[str, Any]          =			{"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

			__UpperCamelCase   :    Optional[Any]          =			False
			__UpperCamelCase   :    str          =			False
			__UpperCamelCase   :    Any          =			False
			__UpperCamelCase   :    List[Any]          =			False

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				Tuple						=    ViTMAEModelTester(self   )
							UpperCamelCase_:				int						=    ConfigTester(self    ,						config_class=snake_case_    ,						has_text_modality=snake_case_    ,						hidden_size=37   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							self.config_tester.run_common_tests()

			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""   )
			def    lowerCAmelCase__						(							self			:							int   ):
							pass

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_, UpperCamelCase_:				int						=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:				Optional[Any]						=    model_class(snake_case_   )
											self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)   )
											UpperCamelCase_:				Union[str, Any]						=    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(snake_case_    ,						nn.Linear   )   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_, UpperCamelCase_:				Dict						=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:				Dict						=    model_class(snake_case_   )
											UpperCamelCase_:				Union[str, Any]						=    inspect.signature(model.forward   )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCamelCase_:				Any						=    [*signature.parameters.keys()]

											UpperCamelCase_:				Tuple						=    ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							UpperCamelCase_:				Optional[Any]						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				str						=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_pretraining(*snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							List[str]    ,						snake_case_			:							List[str]    ,						snake_case_			:							str   ):
							# make masks reproducible
							np.random.seed(2   )

							UpperCamelCase_:				Dict						=    int((pt_model.config.image_size // pt_model.config.patch_size) ** 2   )
							UpperCamelCase_:				int						=    np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
							UpperCamelCase_:				Optional[Any]						=    torch.from_numpy(snake_case_   )

							# Add `noise` argument.
							# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
							UpperCamelCase_:				Dict						=    pt_noise

							super().check_pt_tf_models(snake_case_    ,						snake_case_    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							Dict   ):
							UpperCamelCase_, UpperCamelCase_:				Any						=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											UpperCamelCase_:				Any						=    model_class(snake_case_   )
											model.to(snake_case_   )
											model.eval()
											# make random mask reproducible
											torch.manual_seed(2   )
											with torch.no_grad():
															UpperCamelCase_:				int						=    model(**self._prepare_for_class(snake_case_    ,						snake_case_   )   )

											UpperCamelCase_:				Union[str, Any]						=    outputs[0].cpu().numpy()
											UpperCamelCase_:				Any						=    0

											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(snake_case_   )
															UpperCamelCase_:				Union[str, Any]						=    model_class.from_pretrained(snake_case_   )
															model.to(snake_case_   )
															# make random mask reproducible
															torch.manual_seed(2   )
															with torch.no_grad():
																			UpperCamelCase_:				List[Any]						=    model(**self._prepare_for_class(snake_case_    ,						snake_case_   )   )

															# Make sure we don't have nans
															UpperCamelCase_:				List[Any]						=    after_outputs[0].cpu().numpy()
															UpperCamelCase_:				Optional[Any]						=    0
															UpperCamelCase_:				List[str]						=    np.amax(np.abs(out_a - out_a   )   )
															self.assertLessEqual(snake_case_    ,						1e-5   )

			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""   )
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							pass

			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""   )
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							pass

			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""   )
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							pass

			@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""   )
			def    lowerCAmelCase__						(							self			:							int   ):
							pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							pass




			@slow
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCamelCase_:				Optional[Any]						=    ViTMAEModel.from_pretrained(snake_case_   )
											self.assertIsNotNone(snake_case_   )







def 							A__       (    )	->						List[str]:
				UpperCamelCase_:				Tuple						=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
				return image





@require_torch
@require_vision
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			@cached_property
			def    lowerCAmelCase__						(							self			:							List[str]   ):
							return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""   ) if is_vision_available() else None




			@slow
			def    lowerCAmelCase__						(							self			:							Dict   ):
							# make random mask reproducible across the PT and TF model
							np.random.seed(2   )

							UpperCamelCase_:				Tuple						=    ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""   ).to(snake_case_   )

							UpperCamelCase_:				Tuple						=    self.default_image_processor
							UpperCamelCase_:				Any						=    prepare_img()
							UpperCamelCase_:				Optional[int]						=    image_processor(images=snake_case_    ,						return_tensors="""pt"""   ).to(snake_case_   )

							# prepare a noise vector that will be also used for testing the TF model
							# (this way we can ensure that the PT and TF models operate on the same inputs)
							UpperCamelCase_:				Dict						=    ViTMAEConfig()
							UpperCamelCase_:				List[str]						=    int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
							UpperCamelCase_:				Optional[Any]						=    np.random.uniform(size=(1, num_patches)   )

							# forward pass
							with torch.no_grad():
											UpperCamelCase_:				Optional[Any]						=    model(**snake_case_    ,						noise=torch.from_numpy(snake_case_   ).to(device=snake_case_   )   )

							# verify the logits
							UpperCamelCase_:				List[Any]						=    torch.Size((1, 196, 768)   )
							self.assertEqual(outputs.logits.shape    ,						snake_case_   )

							UpperCamelCase_:				Dict						=    torch.tensor(
							    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]   )

							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,						expected_slice.to(snake_case_   )    ,						atol=1e-4   )   )








import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    str          =			IFInpaintingSuperResolutionPipeline
			__UpperCamelCase   :    str          =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase   :    Optional[Any]          =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}	)
			__UpperCamelCase   :    Dict          =			PipelineTesterMixin.required_optional_params - {"""latents"""}

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							return self._get_superresolution_dummy_components()

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							str=0   ):
							if str(snake_case_   ).startswith("""mps"""   ):
											UpperCamelCase_:				Union[str, Any]						=    torch.manual_seed(snake_case_   )
							else:
											UpperCamelCase_:				Tuple						=    torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

							UpperCamelCase_:				Dict						=    floats_tensor((1, 3, 16, 16)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				int						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )
							UpperCamelCase_:				str						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(snake_case_   )   ).to(snake_case_   )

							UpperCamelCase_:				Dict						=    {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """original_image""": original_image,
							    """mask_image""": mask_image,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,						reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,						)
			def    lowerCAmelCase__						(							self			:							Tuple   ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,						reason="""float16 requires CUDA"""   )
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )

			def    lowerCAmelCase__						(							self			:							str   ):
							self._test_save_load_local()




			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2    ,						)






